[Model] add dna model (#214)

* add dna model

* update dna

* remove some code

---------

Co-authored-by: BuptTab <[email protected]>

examples/dna/dna_trainer.py

@@ -0,0 +1,144 @@
+import os
+# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+# os.environ['TL_BACKEND'] = 'torch'
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 
+# 0:Output all; 1:Filter out INFO; 2:Filter out INFO and WARNING; 3:Filter out INFO, WARNING, and ERROR
+
+import argparse
+import tensorlayerx as tlx
+from gammagl.datasets import Planetoid
+from gammagl.utils import add_self_loops, mask_to_index
+from tensorlayerx.model import TrainOneStep, WithLoss
+from gammagl.models import DNAModel
+from sklearn.model_selection import StratifiedKFold
+import numpy as np
+
+
+class SemiSpvzLoss(WithLoss):
+    def __init__(self, net, loss_fn):
+        super(SemiSpvzLoss, self).__init__(backbone=net, loss_fn=loss_fn)
+
+    def forward(self, data, y):
+        logits = self.backbone_network(data['x'], data['edge_index'])
+        train_logits = tlx.gather(logits, data['train_idx'])
+        train_y = tlx.gather(data['y'], data['train_idx'])
+        loss = self._loss_fn(train_logits, train_y)
+        return loss
+
+
+def calculate_acc(logits, y, metrics):
+    """
+    Args:
+        logits: node logits
+        y: node labels
+        metrics: tensorlayerx.metrics
+
+    Returns:
+        rst
+    """
+
+    metrics.update(logits, y)
+    rst = metrics.result()
+    metrics.reset()
+    return rst
+
+def gen_uniform_20_20_60_split(data):
+    skf = StratifiedKFold(5, shuffle=True, random_state=55)
+    data.y = tlx.convert_to_numpy(data.y)
+    idx = [tlx.convert_to_tensor(i) for _, i in skf.split(data.y, data.y)]
+    data.train_idx = tlx.convert_to_tensor(idx[0], dtype=tlx.int64)
+    data.val_idx = tlx.convert_to_tensor(idx[1], dtype=tlx.int64)
+    data.test_idx = tlx.convert_to_tensor(tlx.concat(idx[2:], axis=0), dtype=tlx.int64)
+    data.y = tlx.convert_to_tensor(data.y)
+    return data
+
+
+def main(args):
+    # load datasets
+    if str.lower(args.dataset) not in ['cora','pubmed','citeseer']:
+        raise ValueError('Unknown dataset: {}'.format(args.dataset))
+    dataset = Planetoid(args.dataset_path, args.dataset)
+    graph = dataset[0]
+    graph = gen_uniform_20_20_60_split(graph)
+
+    net = DNAModel(in_channels=dataset.num_node_features,
+                   hidden_channels=args.hidden_dim,
+                   out_channels=dataset.num_classes,
+                   num_layers=args.num_layers,
+                   drop_rate_conv=args.drop_rate_conv,
+                   drop_rate_model=args.drop_rate_model,
+                   heads=args.heads,
+                   groups=args.groups,
+                   name="DNA")
+
+    optimizer = tlx.optimizers.Adam(lr=args.lr, weight_decay=args.l2_coef)
+    metrics = tlx.metrics.Accuracy()
+    train_weights = net.trainable_weights
+
+    loss_func = SemiSpvzLoss(net, tlx.losses.softmax_cross_entropy_with_logits)
+    train_one_step = TrainOneStep(loss_func, optimizer, train_weights)
+
+    data = {
+        "x": graph.x,
+        "y": graph.y,
+        "edge_index": graph.edge_index,
+        # "edge_weight": edge_weight,
+        "train_idx": graph.train_idx,
+        "test_idx": graph.test_idx,
+        "val_idx": graph.val_idx,
+        "num_nodes": graph.num_nodes,
+    }
+
+    best_val_acc = 0
+    for epoch in range(args.n_epoch):
+        net.set_train()
+        train_loss = train_one_step(data, graph.y)
+        net.set_eval()
+        logits = net(data['x'], data['edge_index'])
+        val_logits = tlx.gather(logits, data['val_idx'])
+        val_y = tlx.gather(data['y'], data['val_idx'])
+        val_acc = calculate_acc(val_logits, val_y, metrics)
+
+        print("Epoch [{:0>3d}] ".format(epoch+1)\
+              + "  train loss: {:.4f}".format(train_loss.item())\
+              + "  val acc: {:.4f}".format(val_acc))
+
+        # save best model on evaluation set
+        if val_acc > best_val_acc:
+            best_val_acc = val_acc
+            net.save_weights(args.best_model_path+net.name+".npz", format='npz_dict')
+
+    net.load_weights(args.best_model_path+net.name+".npz", format='npz_dict')
+    net.set_eval()
+    logits = net(data['x'], data['edge_index'])
+    test_logits = tlx.gather(logits, data['test_idx'])
+    test_y = tlx.gather(data['y'], data['test_idx'])
+    test_acc = calculate_acc(test_logits, test_y, metrics)
+    print("Test acc:  {:.4f}".format(test_acc))
+
+
+if __name__ == '__main__':
+    # parameters setting
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--lr", type=float, default=0.005, help="learnin rate")
+    parser.add_argument("--n_epoch", type=int, default=200, help="number of epoch")
+    parser.add_argument("--hidden_dim", type=int, default=128, help="dimention of hidden layers")
+    parser.add_argument("--drop_rate_conv", type=float, default=0.8, help="drop_rate_conv")
+    parser.add_argument("--drop_rate_model", type=float, default=0.8, help="drop_rate_model")
+    parser.add_argument("--num_layers", type=int, default=4, help="number of layers")
+    parser.add_argument("--heads", type=int, default=8, help="number of heads for stablization")
+    parser.add_argument("--groups", type=int, default=16, help="number of groups")
+    parser.add_argument("--l2_coef", type=float, default=5e-5, help="l2 loss coeficient")
+    parser.add_argument('--dataset', type=str, default='cora', help='dataset')
+    parser.add_argument("--dataset_path", type=str, default=r'', help="path to save dataset")
+    parser.add_argument("--best_model_path", type=str, default=r'./', help="path to save best model")
+    parser.add_argument("--self_loops", type=int, default=1, help="number of graph self-loop")
+    parser.add_argument("--gpu", type=int, default=6)
+
+    args = parser.parse_args()
+    if args.gpu >= 0:
+        tlx.set_device("GPU", args.gpu)
+    else:
+        tlx.set_device("CPU")
+
+    main(args)

examples/dna/readme.md

@@ -0,0 +1,45 @@
+# JUST JUMP: DYNAMIC NEIGHBORHOOD AGGREGATION IN GRAPH NEURAL NETWORKS (DNA)
+
+- Paper link: [https://arxiv.org/abs/1904.04849](https://arxiv.org/abs/1904.04849)
+- The implementation of PyG: [https://github.com/pyg-team/pytorch_geometric/blob/master/examples/dna.py](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/dna.py). 
+
+# Dataset Statics
+
+| Dataset  | # Nodes | # Edges | # Classes |
+|----------|---------|---------|-----------|
+| Cora     | 2,708   | 10,556  | 7         |
+| Citeseer | 3,327   | 9,228   | 6         |
+| Pubmed   | 19,717  | 88,651  | 3         |
+
+Refer to [Planetoid](https://gammagl.readthedocs.io/en/latest/api/gammagl.datasets.html#gammagl.datasets.Planetoid).
+
+Results
+-------
+
+```bash
+# available dataset: "cora", "citeseer", "pubmed"
+
+TL_BACKEND="torch" python dna_trainer.py --dataset cora --lr 0.01 --drop_rate_conv 0.2 --drop_rate_model 0.8 --num_layers 3 --heads 64 --groups 1 --l2_coef 5e-5 --hidden_dim 256
+TL_BACKEND="torch" python dna_trainer.py --dataset cora --lr 0.01 --drop_rate_conv 0.2 --drop_rate_model 0.9 --num_layers 3 --heads 32 --groups 8 --l2_coef 5e-5 --hidden_dim 256
+TL_BACKEND="torch" python dna_trainer.py --dataset cora --lr 0.01 --drop_rate_conv 0.1 --drop_rate_model 0.8 --num_layers 4 --heads 8 --groups 16 --l2_coef 5e-5 --hidden_dim 128
+TL_BACKEND="torch" python dna_trainer.py --dataset citeseer --lr 0.01 --drop_rate_conv 0.2 --drop_rate_model 0.8 --num_layers 3 --heads 32 --groups 1 --l2_coef 5e-5 --hidden_dim 128
+TL_BACKEND="torch" python dna_trainer.py --dataset citeseer --lr 0.01 --drop_rate_conv 0.1 --drop_rate_model 0.8 --num_layers 4 --heads 8 --groups 8 --l2_coef 5e-5 --hidden_dim 128
+TL_BACKEND="torch" python dna_trainer.py --dataset citeseer --lr 0.01 --drop_rate_conv 0.1 --drop_rate_model 0.8 --num_layers 4 --heads 8 --groups 16 --l2_coef 5e-5 --hidden_dim 128
+TL_BACKEND="torch" python dna_trainer.py --dataset pubmed --lr 0.01 --drop_rate_conv 0.1 --drop_rate_model 0.8 --num_layers 4 --heads 8 --groups 1 --l2_coef 5e-5 --hidden_dim 128
+TL_BACKEND="torch" python dna_trainer.py --dataset pubmed --lr 0.01 --drop_rate_conv 0.1 --drop_rate_model 0.8 --num_layers 4 --heads 8 --groups 8 --l2_coef 5e-5 --hidden_dim 128
+TL_BACKEND="torch" python dna_trainer.py --dataset pubmed --lr 0.01 --drop_rate_conv 0.1 --drop_rate_model 0.8 --num_layers 4 --heads 8 --groups 16 --l2_coef 5e-5 --hidden_dim 128
+```
+
+| Dataset  | group | Paper      | Our(th)    |
+| -------- | ----- | ---------- | ---------- |
+| cora     | 1     | 83.88±0.50 | 80.50±0.81 |
+| cora     | 8     | 85.86±0.45 | 81.22±0.18 |
+| cora     | 16    | 86.15±0.57 | 82.25±0.4  |
+| citeseer | 1     | 73.37±0.83 | 71.41±1.02 |
+| citeseer | 8     | 74.19±0.66 | 72.29±0.59 |
+| citeseer | 16    | 74.50±0.62 | 72.99±0.68 |
+| pubmed   | 1     | 87.80±0.25 | 87.32±0.52 |
+| pubmed   | 8     | 88.04±0.17 | 87.46±0.25 |
+| pubmed   | 16    | 88.04±0.22 | 87.49±0.11 |
+
+![image-20240703225836573](readme.assets/image-20240703225836573.png)

gammagl/layers/conv/__init__.py

@@ -33,6 +33,7 @@
 from .magcl_conv import MAGCLConv
 from .fusedgat_conv import FusedGATConv
 from .hid_conv import Hid_conv
+from .dna_conv import DNAConv
 __all__ = [
     'MessagePassing',
     'GCNConv',
@@ -68,7 +69,8 @@
     'MAGCLConv',
     'FusedGATConv',
     'Hid_conv',
-    'HEATlayer'
+    'HEATlayer',
+    'DNAConv'
 ]
 
 classes = __all__